# load the needed libraries
library(tidyverse)
library(ggplot2)
library(lubridate)
library(readxl)
library(hrbrthemes)
library(viridis)
library(ggpubr)
library(purrr)
library(plotly)
library(dplyr)
library(RColorBrewer)
library(stargazer)
knitr::opts_chunk$set(echo = TRUE, warning=FALSE, message=FALSE)
#source('readin_lib.R')
#source('student_perf_lib.R')ELA_Standards<-read_csv("data_604/State_Standards-_Mastery_Star_2425_ELA.csv")%>%
mutate(Division = case_when(
Grade == 9 | Grade == 10 ~ "HS",
Grade == 5 | Grade == 6 | Grade == 7 | Grade == 8 ~ "MS"
))%>%
mutate(Grade = as.factor(Grade))%>%
mutate(`Reporting Category` = as.factor(`Reporting Category`))%>%
mutate(`Domain` = as.factor(`Domain`))%>%
mutate(`Division` = as.factor(`Division`))%>%
mutate(`Screening Window` = as.factor(`Screening Window`))%>%
mutate(`Text Type` = as.factor(`Text Type`))
ELA_Standardsstar_dem_all_private<-read_csv("data_604/student_star_dem_all_private.csv")%>%
mutate(`Grade` = as.factor(`Grade`))%>%
mutate(`Grade` = recode_factor(`Grade`,
"5" = "5",
"6" = "6",
"7" = "7",
"8"= "8",
"9"= "9",
"10"= "10",
.ordered = TRUE))%>%
mutate(School_Year = as.factor(School_Year))%>%
mutate(School_Year = recode_factor(School_Year,
"2023-2024" = "2023-2024",
"2024-2025" = "2024-2025",
.ordered = TRUE))%>%
mutate(`Assignment Type` = as.factor(`Assignment Type`))%>%
mutate(`Test 1 Benchmark Category` = recode_factor(`Test 1 Benchmark Category`,
"At/Above Benchmark" = "At/Above Benchmark",
"On Watch" = "On Watch",
"Intervention" = "Intervention",
"Urgent Intervention"= "Urgent Intervention",
.ordered = TRUE))%>%
mutate(IEP_Status = as.factor(IEP_Status))%>%
mutate(`504_Status` = as.factor(`504_Status`))%>%
mutate(`Low_Income_Status` = as.factor(Low_Income_Status))%>%
mutate(`Gender` = as.factor(Gender) )%>%
mutate(`Gender_Status` = case_when(
Gender == "Male" ~ 0,
Gender == "Female" ~ 1
))
star_dem_all_privateHistSchoolBenchSum<-read_csv("data_604/StarBenchmarkSummary_School.csv")%>%
mutate(Year = as.factor(Year))%>%
mutate(`Screening Window` = as.factor(`Screening Window`))%>%
mutate(`Benchmark_Status` = recode_factor(`Benchmark_Status`,
"At/Above Benchmark" = "At/Above Benchmark",
"Below Benchmark" = "Below Benchmark",
.ordered = TRUE))%>%
select(Year, `Screening Window`, Subject, `Benchmark_Status`, `% Students`)
HistSchoolBenchSumHistGradeGrowthSum<-read_csv("data_604/StarBenchmarkSummary_SGP_Sum.csv")%>%
mutate(Year = as.factor(Year))%>%
mutate(`Screening Window` = as.factor(`Screening Window`))%>%
mutate(`Subject` = as.factor(`Subject`))%>%
mutate(`Grade` = as.factor(Grade))%>%
mutate(`Grade` = recode_factor(`Grade`,
"Grade 5" = "Grade 5",
"Grade 6" = "Grade 6",
"Grade 7" = "Grade 7",
"Grade 8"= "Grade 8",
"Grade 9"= "Grade 9",
"Grade 10"= "Grade 10",
.ordered = TRUE))%>%
select(Year, `Screening Window`, `Grade`, Subject, `Median SGP`)
HistGradeGrowthSumAre Rising Tide students making progress toward our achievement accountability goal: “80% of Rising Students will test as at or above Benchmark in Reading on the Renaissance Star Reading and Star Mathematics screening assessments.?
star_dem_all_private<-star_dem_all_private%>%
mutate(Benchmark_Status =
case_when(`Test 1 PR` >= 40 ~ "At/Above Benchmark",
`Test 1 PR` < 40 ~ "Below Benchmark"))%>%
mutate(Benchmark_Status = as.factor(Benchmark_Status))star_dem_math<- star_dem_all_private%>%
filter(`Assignment Type` == "Star Math")
fit_Math_achieve = lm(`Test 1 PR` ~ School_Year + `IEP_Status`, data = star_dem_math)
summary(fit_Math_achieve)
Call:
lm(formula = `Test 1 PR` ~ School_Year + IEP_Status, data = star_dem_math)
Residuals:
Min 1Q Median 3Q Max
-61.191 -17.191 1.701 18.453 69.453
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 64.2450 0.8557 75.076 <2e-16 ***
School_Year.L 2.7524 1.0469 2.629 0.0087 **
IEP_Status1 -32.7515 1.7054 -19.205 <2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 22.95 on 958 degrees of freedom
Multiple R-squared: 0.2811, Adjusted R-squared: 0.2796
F-statistic: 187.3 on 2 and 958 DF, p-value: < 2.2e-16#star_dem_mathfit_Math_growth = lm(`SGP (Expectation=50)` ~ `Test 1 PR` + Gender + `IEP_Status` + Grade, data = star_dem_math)
summary(fit_Math_growth)
Call:
lm(formula = `SGP (Expectation=50)` ~ `Test 1 PR` + Gender +
IEP_Status + Grade, data = star_dem_math)
Residuals:
Min 1Q Median 3Q Max
-63.723 -25.503 1.389 24.956 61.608
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 57.92866 2.87574 20.144 < 2e-16 ***
`Test 1 PR` -0.08022 0.04142 -1.937 0.053064 .
GenderMale 4.49068 1.91905 2.340 0.019487 *
GenderNon-Binary 9.66558 14.64207 0.660 0.509334
IEP_Status1 -9.58796 2.57071 -3.730 0.000203 ***
Grade.L 7.85183 2.44271 3.214 0.001351 **
Grade.Q 0.52935 2.34611 0.226 0.821540
Grade.C 3.78049 2.37354 1.593 0.111545
Grade^4 1.69811 2.33349 0.728 0.466968
Grade^5 -12.76610 2.19541 -5.815 8.28e-09 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 28.98 on 951 degrees of freedom
Multiple R-squared: 0.06603, Adjusted R-squared: 0.05719
F-statistic: 7.47 on 9 and 951 DF, p-value: 1.32e-10star_dem_ELA<- star_dem_all_private%>%
filter(`Assignment Type` == "Star Reading")
fit_ELA_achieve = lm(`Test 1 PR` ~ School_Year + `IEP_Status`, data = star_dem_ELA)
summary(fit_ELA_achieve)
Call:
lm(formula = `Test 1 PR` ~ School_Year + IEP_Status, data = star_dem_ELA)
Residuals:
Min 1Q Median 3Q Max
-58.281 -19.490 0.346 19.346 61.510
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 60.9676 0.8989 67.82 < 2e-16 ***
School_Year.L 3.2721 1.0980 2.98 0.00296 **
IEP_Status1 -26.1643 1.7833 -14.67 < 2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 24.1 on 961 degrees of freedom
Multiple R-squared: 0.1884, Adjusted R-squared: 0.1868
F-statistic: 111.6 on 2 and 961 DF, p-value: < 2.2e-16fit_ELA_growth = lm(`SGP (Expectation=50)` ~ `IEP_Status` + School_Year + Grade, data = star_dem_ELA)
summary(fit_ELA_growth)
Call:
lm(formula = `SGP (Expectation=50)` ~ IEP_Status + School_Year +
Grade, data = star_dem_ELA)
Residuals:
Min 1Q Median 3Q Max
-55.625 -26.710 0.996 27.187 52.908
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 53.9204 1.1346 47.523 <2e-16 ***
IEP_Status1 -0.4319 2.2199 -0.195 0.8458
School_Year.L -0.7311 1.3673 -0.535 0.5930
Grade.L -1.8450 2.5115 -0.735 0.4627
Grade.Q 5.5437 2.4150 2.296 0.0219 *
Grade.C 0.2302 2.4479 0.094 0.9251
Grade^4 -5.8960 2.4076 -2.449 0.0145 *
Grade^5 -4.6709 2.2690 -2.059 0.0398 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 30 on 956 degrees of freedom
Multiple R-squared: 0.01824, Adjusted R-squared: 0.01105
F-statistic: 2.537 on 7 and 956 DF, p-value: 0.01369Bench_Sum_Plot<-HistSchoolBenchSum%>%
filter(`Year` == "2024-2025" & `Screening Window` == "Winter" | `Year` == "2023-2024" & `Screening Window` == "Spring")%>%
filter(`Benchmark_Status` == "At/Above Benchmark")%>%
ggplot(aes(x=`Benchmark_Status`,fill = `Year`, y = `% Students`)) +
geom_bar(position="dodge", stat = "identity") +
geom_text(aes( y = `% Students`, label = `% Students`,
vjust = -.25), position = position_dodge(width = 1)) +
#theme_classic() +
theme(axis.title.x=element_blank(),
# axis.text.x = element_text(angle = 45, , hjust = 1),
axis.ticks.x=element_blank())+
scale_fill_brewer(palette = "Paired")+
labs(
y = "% Students",
x= "Benchmark Category",
title = "Rising Tide Benchmark Achievement by School Year",
caption = "Source: Renaissance Star Literacy and Math Screening")+
geom_hline(yintercept = 80, color = "grey")+
annotate("text", x = .75, y = 85, label = "80% Goal") +
facet_wrap(~Subject)#| eval: false
#| fig-cap: "Rising Tide Benchmark Achievement by School Year, Renaissance Star Literacy and Math"
#| fig-alt: >
#| A bar blot with school year (ordered, factor) on the x-axis and the percentage of students
#| classified as At/Above benchmark on the y-axis, faceted by subject (English Language Arts or Math)
#| Plot illustrates the relationship between the three variables.
Bench_Sum_Plot
Growth_Sum_ELA<-star_dem_all_private%>%
filter(`School_Year` == "2024-2025")%>%
filter(`Assignment Type` == "Star Reading")%>%
group_by(Grade)%>%
summarize(median_SGP= median(`SGP (Expectation=50)`))
Growth_Sum_ELA_Plot<-ggplot(Growth_Sum_ELA, aes(x=`Grade`, y = `median_SGP`,fill="skyblue")) +
geom_bar(position="dodge", stat = "identity", fill="skyblue") +
#facet_wrap(~Grade)+
geom_text(aes( y = `median_SGP`, label = `median_SGP`,
vjust = -.25))+
theme_classic()+
#scale_color_brewer(palette = "Blues")+
theme(axis.title.x=element_blank(),
#axis.text.x=element_blank(),
axis.ticks.x=element_blank())+
labs(
y = "Median SGP",
x= "Grade Level",
title = "Literacy Student Growth Percentile by Grade Level",
caption = "Source: 2024-2025 Renaissance Star Screening")+
geom_hline(yintercept = 50, color = "grey")+
annotate("text", x = 1., y = 52, label = "50th Percentile")Growth_Sum_ELA_PlotHist_Growth_Sum_ELA_Plot<-HistGradeGrowthSum%>%
filter(Subject == "ELA")%>%
filter(`Screening Window` == "Fall-Winter")%>%
ggplot(aes(x=`Grade`,fill = "skyblue", y = `Median SGP`)) +
geom_bar(position="dodge", stat = "identity", fill = "skyblue") +
#facet_wrap(~Grade)+
geom_text(aes( y = `Median SGP`, label = `Median SGP`,
vjust = -.25), position = position_dodge(width = 1))+
#theme_classic()+
#scale_fill_brewer(palette = "Blues")+
theme(axis.title.x=element_blank(),
#axis.text.x=element_blank(),
axis.ticks.x=element_blank())+
labs(
y = "Median SGP",
x= "Grade Level",
title = "Literacy Student Growth Percentile by Grade Level",
caption = "Source: Renaissance Star Literacy Screening")+
geom_hline(yintercept = 50, color = "grey")+
annotate("text", x = 1.55, y = 52, label = "50th Percentile")+
facet_wrap(~Year)Hist_Growth_Sum_ELA_PlotGrowth_Sum_Math<-star_dem_all_private%>%
filter(`Assignment Type` == "Star Math")%>%
filter(`School_Year` == "2024-2025")%>%
group_by(Grade)%>%
summarize(median_SGP= median(`SGP (Expectation=50)`))
Growth_Sum_Math_Plot<-ggplot(Growth_Sum_Math, aes(x=`Grade`,fill = `Grade`, y = `median_SGP`, fill="skyblue")) +
geom_bar(position="dodge", stat = "identity", fill="skyblue") +
#facet_wrap(~Grade)+
geom_text(aes( y = `median_SGP`, label = `median_SGP`,
vjust = -.25))+
theme_classic()+
scale_fill_brewer(palette = "Blues")+
theme(axis.title.x=element_blank(),
axis.text.x=element_blank(),
axis.ticks.x=element_blank())+
labs(
y = "Median SGP",
x= "Grade Level",
title = "Math Student Growth Percentile by Grade Level",
caption = "Source: 2024-2025 Renaissance Star Math Screening")+
geom_hline(yintercept = 50, color = "grey")+
annotate("text", x = 1.05, y = 53, label = "50th Percentile")Growth_Sum_Math_PlotHist_Growth_Sum_Math_Plot<-HistGradeGrowthSum%>%
filter(Subject == "Math")%>%
filter(`Screening Window` == "Fall-Winter")%>%
ggplot(aes(x=`Grade`,fill = "skyblue", y = `Median SGP`)) +
geom_bar(position="dodge", stat = "identity", fill = "skyblue") +
#facet_wrap(~Grade)+
geom_text(aes( y = `Median SGP`, label = `Median SGP`,
vjust = -.25), position = position_dodge(width = 1))+
#theme_classic()+
#scale_fill_brewer(palette = "Blues")+
theme(axis.title.x=element_blank(),
#axis.text.x=element_blank(),
axis.ticks.x=element_blank())+
labs(
y = "Median SGP",
x= "Grade Level",
title = "Math Student Growth Percentile by Grade Level",
caption = "Source: Renaissance Star Math Screening")+
geom_hline(yintercept = 50, color = "grey")+
annotate("text", x = 5.5, y = 48, label = "50th Percentile")+
facet_wrap(~Year)Hist_Growth_Sum_Math_PlotIs there a relationship between Rising Tide’s Grade 5 Mathematics curriculum initiatives and Grade 5 student growth in mathematics?
Math_IEP_Sum<-star_dem_math%>%
mutate(IEP = case_when(IEP_Status == 1 ~ "Yes",
IEP_Status == 0 ~ "No"))%>%
filter(`Assignment Type` == "Star Math")%>%
group_by(Grade, `School_Year`, IEP)%>%
summarize(`Median SGP` = median(`SGP (Expectation=50)`))
Math_IEP_SumMath_Gen_Sum<-star_dem_math%>%
filter(`Assignment Type` == "Star Math")%>%
group_by(Grade, `School_Year`, Gender)%>%
summarize(`Median SGP` = median(`SGP (Expectation=50)`))
Math_Gen_Sumstar_dem_mathMath_Gen_IEP_Sum<-star_dem_math%>%
filter(`Grade` == "5")%>%
group_by(`School_Year`, Gender, IEP_Status)%>%
summarize(`Median SGP` = median(`SGP (Expectation=50)`))
Math_Gen_IEP_SumMath_Gen_IEP_Achieve_Sum<-star_dem_math%>%
filter(`Grade` == "5")%>%
group_by(`School_Year`, Gender, IEP_Status)%>%
summarize(`Median Test 1 PR` = median(`Test 1 PR`))
Math_Gen_IEP_Achieve_Sum # ggplot(aes(Species, fill=ifelse(Species=="setosa", "A","B"))) +
# geom_bar(show.legend=FALSE) +
# scale_fill_manual(values=c(A="blue", B="red"))
Hist_Growth_Sum_G5Math_Plot<-HistGradeGrowthSum%>%
filter(Subject == "Math")%>%
filter(`Screening Window` == "Fall-Winter")%>%
ggplot(aes(x=`Grade`,fill = ifelse(Grade == "5", "A", "B"), y = `Median SGP`)) +
geom_bar(position="dodge", stat = "identity", show.legend=FALSE) +
scale_fill_manual(values = c(A= "skyblue", B = "grey"))+
#facet_wrap(~Grade)+
geom_text(aes( y = `Median SGP`, label = `Median SGP`,
vjust = -.25), position = position_dodge(width = 1))+
#theme_classic()+
#scale_fill_brewer(palette = "Blues")+
theme(axis.title.x=element_blank(),
#axis.text.x=element_blank(),
axis.ticks.x=element_blank())+
labs(
y = "Median SGP",
x= "Grade Level",
title = "Math Student Growth Percentile by Grade Level",
caption = "Source: Renaissance Star Math Screening")+
geom_hline(yintercept = 50, color = "grey")+
annotate("text", x = 5.5, y = 48, label = "50th Percentile")+
facet_wrap(~Year)Hist_Growth_Sum_G5Math_PlotHist_Growth_G5Math_Plot<-HistGradeGrowthSum%>%
filter(Subject == "Math")%>%
filter(Grade == "5")%>%
filter(`Screening Window` == "Fall-Winter")%>%
ggplot(aes(x=`Year`,fill = "skyblue", y = `Median SGP`)) +
geom_bar(position="dodge", stat = "identity", fill = "skyblue") +
#facet_wrap(~Grade)+
geom_text(aes( y = `Median SGP`, label = `Median SGP`,
vjust = -.25), position = position_dodge(width = 1))+
theme_classic()+
scale_fill_brewer(palette = "Paired")+
theme(axis.title.x=element_blank(),
#axis.text.x=element_blank(),
axis.ticks.x=element_blank())+
labs(
y = "Median SGP",
x= "School Year",
title = "Improvement in Grade 5 Student Growth Percentile",
caption = "Source: Renaissance Star Math Screening")+
geom_hline(yintercept = 50, color = "grey")+
annotate("text", x = 1., y = 52, label = "Goal 50th Percentile")#+
#theme_classic()
#facet_wrap(~Year)Hist_Growth_G5Math_Plot IEP_Growth_G5Math_Plot<- Math_IEP_Sum%>%
filter(Grade == "5")%>%
ggplot(aes(x=`IEP`,fill = `School_Year`, y = `Median SGP`)) +
geom_bar(position="dodge", stat = "identity") +
geom_text(aes( y = `Median SGP`, x = `IEP`, label = `Median SGP`), position = position_dodge(width=.9),
vjust = 1)+
theme_classic()+
theme(#axis.title.x=element_blank(),
#axis.text.x=element_blank(),
# axis.text.x=element_text(angle =45, hjust = 1),
axis.ticks.x=element_blank())+
scale_fill_brewer(palette = "Paired")+
# facet_wrap(~`Screening Window`)+
labs(
y = "Median SGP",
x= "IEP Status",
title = "Improved Growth For All Grade 5 Learners",
caption = "Source: Renaissance Star Math Screening")+
geom_hline(yintercept = 50, color = "grey")+
annotate("text", x =.70, y = 52, label = "Goal 50th Percentile")#+IEP_Growth_G5Math_PlotGen_Growth_G5Math_Plot<- Math_Gen_Sum%>%
filter(Grade == "5")%>%
ggplot(aes(x=`Gender`,fill = `School_Year`, y = `Median SGP`)) +
geom_bar(position="dodge", stat = "identity") +
geom_text(aes( y = `Median SGP`, x = `Gender`, label = `Median SGP`), position = position_dodge(width=.9),
vjust = 1)+
theme_classic()+
theme(axis.title.x=element_blank(),
#axis.text.x=element_blank(),
# axis.text.x=element_text(angle =45, hjust = 1),
axis.ticks.x=element_blank())+
scale_fill_brewer(palette = "Paired")+
# facet_wrap(~`Screening Window`)+
labs(
y = "Median SGP",
x= "Gender",
title = "Improved Growth For All Grade 5 Learners",
caption = "Source: Renaissance Star Screening")+
geom_hline(yintercept = 50, color = "grey")+
annotate("text", x =.70, y = 52, label = "Goal 50th Percentile")#+Gen_Growth_G5Math_PlotIs there a relationship between the text type of a literacy standard (informational vs. literature) and Rising Tide High School students’ mastery of the standard?
ELA_StandardsELA_Text_Summary<-ELA_Standards%>%
filter(`Reporting Category` == "Reading")%>%
group_by(`Text Type`, `Screening Window`)%>%
summarize(`Mean % Secure` = round(mean(`% Secure`)),
`Median % Secure` = round(median(`% Secure`)))
ELA_Text_SummaryELA_Text_Summary%>%
filter(`Screening Window` != "Spring")%>%
ggplot(aes(x=`Text Type`,fill = `Text Type`, y = `Mean % Secure`)) +
geom_bar(position="dodge", stat = "identity") +
geom_text(aes( y = `Mean % Secure`, x = `Text Type`, label = `Mean % Secure`), position = position_dodge(width=.9),
vjust = 1)+
theme(axis.title.x=element_blank(),
#axis.text.x=element_blank(),
axis.text.x=element_text(angle =45, hjust = 1),
axis.ticks.x=element_blank())+
scale_fill_brewer(palette = "Paired")+
facet_wrap(~`Screening Window`)+
labs(
y = "Mean % Secure",
x= "Domainl",
title = "School State Standard Mastery by Text Type ELA",
caption = "2024-2025 Star Reading Screening")
ELA_Text_Domain_Summary<-ELA_Standards%>%
filter(`Reporting Category` == "Reading")%>%
group_by(`Text Type`, `Domain`, `Screening Window`)%>%
summarize(`Mean % Secure` = round(mean(`% Secure`)),
`Median % Secure` = round(median(`% Secure`)))
ELA_Text_Domain_SummaryELA_Text_Div_Summary<-ELA_Standards%>%
filter(`Reporting Category` == "Reading")%>%
group_by(`Text Type`, `Division`, `Screening Window`)%>%
summarize(`Mean % Secure` = round(mean(`% Secure`)),
`Median % Secure` = round(median(`% Secure`)))
ELA_Text_Div_SummaryELA_Text_Div_Summary%>%
filter(`Screening Window` != "Spring")%>%
ggplot(aes(x=`Text Type`,fill = `Screening Window`, y = `Mean % Secure`)) +
geom_bar(position="dodge", stat = "identity") +
geom_text(aes( y = `Mean % Secure`, x = `Text Type`, label = `Mean % Secure`), position = position_dodge(width=.9),
vjust = 1)+
theme(axis.title.x=element_blank(),
#axis.text.x=element_blank(),
axis.text.x=element_text(angle =45, hjust = 1),
axis.ticks.x=element_blank())+
scale_fill_brewer(palette = "Paired")+
facet_wrap(~`Division`)+
labs(
y = "Mean % Secure",
x= "Domainl",
title = "School State Standard Mastery by Text Type ELA",
caption = "2024-2025 Star Reading Screening")
ELA_Text_Domain_Summary%>%
filter(`Screening Window` != "Spring")%>%
ggplot(aes(x=`Domain`,fill = `Text Type`, y = `Mean % Secure`)) +
geom_bar(position="dodge", stat = "identity") +
geom_text(aes( y = `Mean % Secure`, x = `Domain`, label = `Mean % Secure`), position = position_dodge(width=.9),
vjust = 1)+
theme(axis.title.x=element_blank(),
#axis.text.x=element_blank(),
axis.text.x=element_text(angle =45, hjust = 1),
axis.ticks.x=element_blank())+
scale_fill_brewer(palette = "Paired")+
facet_wrap(~`Screening Window`)+
labs(
y = "Mean % Secure",
x= "Domainl",
title = "School State Standard Mastery by Text Type ELA",
caption = "2024-2025 Star Reading Screening")
ELA_StandardsELA_TextType_Sum_HS<-ELA_Standards%>%
filter(`Division` == "HS")%>%
filter (`Screening Window` != "Spring")%>%
filter(`Reporting Category` == "Reading")%>%
group_by(`Screening Window`, Grade, `Text Type`)%>%
summarize(`Median % Secure` = round(median(`% Secure`)))
ELA_TextType_Sum_HS%>%
ggplot(aes(x=`Text Type`,fill = `Screening Window`, y = `Median % Secure`)) +
geom_bar(position="dodge", stat = "identity") +
#scale_x_reverse()+
geom_text(aes( y = `Median % Secure`, label = `Median % Secure`,
vjust = -.25), position = position_dodge(width = 1)) +
#theme_classic() +
theme(axis.title.x=element_blank(),
# axis.text.x=element_blank(),
axis.ticks.x=element_blank())+
scale_fill_brewer(palette = "Paired")+
labs(
y = "Median % Secure",
x= "Text Type",
title = "HS Reading Standards Mastery by Text Type",
caption = paste("Fall-Winter Screening", "2024-2025"))+
facet_wrap(~Grade)
ELA_TextType_Sum_MS<-ELA_Standards%>%
filter(`Division` == "MS")%>%
filter (`Screening Window` != "Spring")%>%
filter(`Reporting Category` == "Reading")%>%
group_by(`Screening Window`, Grade, `Text Type`)%>%
summarize(`Median % Secure` = round(median(`% Secure`)))
ELA_TextType_Sum_MS%>%
ggplot(aes(x=`Text Type`,fill = `Screening Window`, y = `Median % Secure`)) +
geom_bar(position="dodge", stat = "identity") +
#scale_x_reverse()+
geom_text(aes( y = `Median % Secure`, label = `Median % Secure`,
vjust = -.25), position = position_dodge(width = 1)) +
#theme_classic() +
theme(axis.title.x=element_blank(),
# axis.text.x=element_blank(),
axis.ticks.x=element_blank())+
scale_fill_brewer(palette = "Paired")+
labs(
y = "Median % Secure",
x= "Text Type",
title = "MS Reading Standards Mastery by Text Type",
caption = paste("Fall-Winter Screening", "2024-2025"))+
facet_wrap(~Grade)
MathHistGrowth<- star_dem_math
G5MathHistGrowth<-star_dem_math%>%
filter(Grade == "5")
G5_Hist_Year <- G5MathHistGrowth %>%
ggplot( aes(x=`SGP (Expectation=50)`, fill=School_Year)) +
geom_histogram( color="#e9ecef", alpha=0.6,binwidth=10, position = 'identity') +
scale_fill_manual(values=c("#69b3a2", "#404080")) +
theme_classic() +
labs(fill="")#+
# facet_wrap(~Year)
G5_Hist_Year
G5_Hist_Year_Facet <- G5MathHistGrowth %>%
ggplot( aes(x=`SGP (Expectation=50)`, fill=School_Year)) +
geom_histogram( color="#e9ecef", alpha=0.6,binwidth=15, position = 'identity') +
scale_fill_manual(values=c("#69b3a2", "#404080")) +
theme_classic() +
labs(fill="")+
labs( y = "",
title = "Grade 5 Student Growth Distribution",
x = "Student Growth Percentile (SGP)",
caption = "Star Math Assessments")+
facet_wrap(~School_Year)
G5_Hist_Year_Facet
Growth_Hist<-MathHistGrowth%>%
ggplot( aes(x= `SGP (Expectation=50)`)) +
#geom_histogram(alpha=0.6, binwidth = 15) +
#ggplot(aes()+
geom_histogram(alpha = .9, binwidth = 15)+
#scale_fill_viridis(discrete=TRUE) +
#scale_color_viridis(discrete=TRUE) +
theme_classic()
Growth_Hist
#summary(MathHistGrowth)
G5MathHistGrowthfit_G5Math_growth = lm(`SGP (Expectation=50)` ~ School_Year + `Test 1 PR`, data = G5MathHistGrowth)
#summary(fit_G5Math_growth)
AIC(fit_G5Math_growth)[1] 1716.643BIC(fit_G5Math_growth)[1] 1729.348fit_Math_growth = lm(`SGP (Expectation=50)` ~ Grade + School_Year + `Test 1 PR` + Grade*School_Year, data = MathHistGrowth)
#summary(fit_Math_growth)
#summary(MathHistGrowth)
MathHistGrowth%>%
summarize(`Mean SGP` = mean(`SGP (Expectation=50)`),
`SD SGP` = sd(`SGP (Expectation=50)`))MathHistGrowth%>%
group_by(Grade)%>%
summarize(`Mean SGP` = mean(`SGP (Expectation=50)`),
`SD SGP` = sd(`SGP (Expectation=50)`))summary(fit_G5Math_growth)
Call:
lm(formula = `SGP (Expectation=50)` ~ School_Year + `Test 1 PR`,
data = G5MathHistGrowth)
Residuals:
Min 1Q Median 3Q Max
-54.538 -26.443 -2.289 28.824 57.371
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 44.30080 5.48156 8.082 1.03e-13 ***
School_Year.L 8.95842 3.27026 2.739 0.0068 **
`Test 1 PR` 0.05907 0.08523 0.693 0.4892
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 30.46 on 174 degrees of freedom
Multiple R-squared: 0.04741, Adjusted R-squared: 0.03646
F-statistic: 4.33 on 2 and 174 DF, p-value: 0.01462stargazer(fit_G5Math_growth, type = "text")
===============================================
Dependent variable:
---------------------------
`SGP (Expectation=50)`
-----------------------------------------------
School_Year.L 8.958***
(3.270)
`Test 1 PR` 0.059
(0.085)
Constant 44.301***
(5.482)
-----------------------------------------------
Observations 177
R2 0.047
Adjusted R2 0.036
Residual Std. Error 30.456 (df = 174)
F Statistic 4.330** (df = 2; 174)
===============================================
Note: *p<0.1; **p<0.05; ***p<0.01plot(fit_G5Math_growth, which = 1:6)
cooks= 4/179
cooks #.0223[1] 0.02234637G5MathHistGrowthG5MathHistGrowth_Scaled<-G5MathHistGrowth%>%
mutate(across(where(is.numeric), scale))
G5MathHistGrowth_Scaledfit_G5Math_growth_scaled = lm(`SGP (Expectation=50)` ~ School_Year + `Test 1 PR`, data = G5MathHistGrowth_Scaled)
summary(fit_G5Math_growth_scaled)
Call:
lm(formula = `SGP (Expectation=50)` ~ School_Year + `Test 1 PR`,
data = G5MathHistGrowth_Scaled)
Residuals:
Min 1Q Median 3Q Max
-1.75778 -0.85226 -0.07379 0.92901 1.84908
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.008074 0.073841 -0.109 0.9131
School_Year.L 0.288731 0.105401 2.739 0.0068 **
`Test 1 PR` 0.051760 0.074683 0.693 0.4892
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.9816 on 174 degrees of freedom
Multiple R-squared: 0.04741, Adjusted R-squared: 0.03646
F-statistic: 4.33 on 2 and 174 DF, p-value: 0.01462AIC(fit_G5Math_growth_scaled)[1] 500.7046BIC(fit_G5Math_growth_scaled)[1] 513.4092stargazer(fit_G5Math_growth_scaled, type = "text")
===============================================
Dependent variable:
---------------------------
`SGP (Expectation=50)`
-----------------------------------------------
School_Year.L 0.289***
(0.105)
`Test 1 PR` 0.052
(0.075)
Constant -0.008
(0.074)
-----------------------------------------------
Observations 177
R2 0.047
Adjusted R2 0.036
Residual Std. Error 0.982 (df = 174)
F Statistic 4.330** (df = 2; 174)
===============================================
Note: *p<0.1; **p<0.05; ***p<0.01##ELA Standards
ELA_StandardsHS_ELA_Standards<-ELA_Standards%>%
filter(Grade == 9|Grade == 10)%>%
filter(`Text Type` == "Informational Text" | `Text Type` == "Literature")
HS_ELA_Standardsfit_ELA_text = lm(`% Secure` ~ ( `Text Type`), data = HS_ELA_Standards)
summary(fit_ELA_text)
Call:
lm(formula = `% Secure` ~ (`Text Type`), data = HS_ELA_Standards)
Residuals:
Min 1Q Median 3Q Max
-25.194 -9.352 -2.510 9.333 41.175
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 44.825 1.966 22.80 < 2e-16 ***
`Text Type`Literature 8.369 2.857 2.93 0.00451 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 12.43 on 74 degrees of freedom
(38 observations deleted due to missingness)
Multiple R-squared: 0.1039, Adjusted R-squared: 0.09183
F-statistic: 8.584 on 1 and 74 DF, p-value: 0.004507fit_ELA_text_domain = lm(`% Secure` ~ ( `Text Type` + `Screening Window` + `Domain`), data = HS_ELA_Standards)
summary(fit_ELA_text_domain)
Call:
lm(formula = `% Secure` ~ (`Text Type` + `Screening Window` +
Domain), data = HS_ELA_Standards)
Residuals:
Min 1Q Median 3Q Max
-28.378 -8.155 -1.182 7.971 40.030
Coefficients:
Estimate Std. Error t value
(Intercept) 39.314 3.175 12.382
`Text Type`Literature 8.840 2.806 3.151
`Screening Window`Winter 4.947 2.791 1.773
DomainIntegration of Knowledge and Ideas 7.276 3.694 1.970
DomainKey Ideas and Details 1.708 3.512 0.486
DomainRange of Reading and Level of Text Complexity 3.417 4.967 0.688
Pr(>|t|)
(Intercept) <2e-16 ***
`Text Type`Literature 0.0024 **
`Screening Window`Winter 0.0806 .
DomainIntegration of Knowledge and Ideas 0.0528 .
DomainKey Ideas and Details 0.6282
DomainRange of Reading and Level of Text Complexity 0.4938
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 12.17 on 70 degrees of freedom
(38 observations deleted due to missingness)
Multiple R-squared: 0.1886, Adjusted R-squared: 0.1307
F-statistic: 3.255 on 5 and 70 DF, p-value: 0.01063HS_ELA_Text_Facet <- HS_ELA_Standards %>%
ggplot( aes(x=`% Secure`, fill=`Text Type`)) +
geom_histogram( color="#e9ecef", alpha=0.6,binwidth=15, position = 'identity') +
scale_fill_manual(values=c("#69b3a2", "#404080")) +
theme_classic() +
labs( y = "",
title = "High School State Standard Mastery Distribution",
x = "% Students Secure",
caption = "Star Reading: State Standards Mastery")+
facet_wrap(~`Text Type`)
HS_ELA_Text_Facet
stargazer(fit_ELA_text_domain, type = "text")
===============================================================================
Dependent variable:
---------------------------
`% Secure`
-------------------------------------------------------------------------------
`Text Type`Literature 8.840***
(2.806)
`Screening Window`Winter 4.947*
(2.791)
DomainIntegration of Knowledge and Ideas 7.276*
(3.694)
DomainKey Ideas and Details 1.708
(3.512)
DomainRange of Reading and Level of Text Complexity 3.417
(4.967)
Constant 39.314***
(3.175)
-------------------------------------------------------------------------------
Observations 76
R2 0.189
Adjusted R2 0.131
Residual Std. Error 12.166 (df = 70)
F Statistic 3.255** (df = 5; 70)
===============================================================================
Note: *p<0.1; **p<0.05; ***p<0.01#
# variable_labels <- c(
# "% Secure", # For the dependent variable
# "Text Type: Informational", # For the reference category
# "Text Type: Literature", # For the first other category
# "Screening Window: Fall", # For the second other category
# "Screening Window: Winter",
# "Domain: Craft and Structure",
# "Domain: Integration of Knolwedge and Ideas",
# "Domain: Key Ideas and Details",
# "Domain: Range of Reading and Level of Text Complexity"
# )
# stargazer(fit_ELA_text_domain, covariate.labels = variable_labels, type = "text")